Application of machine learning methods for estimating and comparing the sulfur dioxide absorption capacity of a variety of deep eutectic solvents

Sulfur dioxide (SO2) is one of the main atmospheric pollutants and an active threat to human health. SO2 separation from industrial flue gases improves air quality, decreases human health problems, and reserves sulfur resources. The capturing and recycling processes demand a green solvent with an effective, selective, and reversible SO2 absorption. Deep eutectic solvents (DESs) have recently been engaged in SO2 capture/recycle processes. The SO2 removal capacity of DESs depends on their ingredients (the hydrogen bond donor, hydrogen bond acceptor, water content), temperature, and pressure. Despite comprehensive experimental investigations, literature presents no clue to compare the SO2 absorption capacity of DESs considering their compositions and operating conditions. Therefore, this work deploys an efficient machine learning model to estimate the SO2 absorption capacity of DESs as a function of their molecular weight, water content, pressure, and temperature. All the laboratory-measured datasets reported in the literature have been included to ensure that the deployed model is reliable and generalized. Furthermore, the proposed model has been selected among five different classes of artificial neural networks. A single hidden-layer neural network with only eleven neurons optimized by the Levenberg-Marquardt is the most precise model predicting 480 DES-SO2 phase equilibria with the mean squared error, mean absolute percentage error, and coefficient of determination of 1.13 x 10(-3), 4.76%, and 0.97936, respectively. This research is the first step toward constructing a reliable model for screening available DESs based on their SO2 absorption capacity and finding the best candidate.

Automated summary

Sulfur dioxide (SO2) is a main atmospheric pollutant harmful to health. Deep eutectic solvents (DESs) play a role in SO2 capture/recycle. Evaluating DESs' SO2 absorption capacity through a machine learning model, selecting the best model for prediction.